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Database System Concepts, 5th Ed.

©Silberschatz, Korth and Sudarshan

See www.db-book.com for conditions on re-use

Chapter 20: Database System Architectures

http://www.db-book.com/






©Silberschatz, Korth and Sudarshan20.2Database System Concepts - 5th Edition, Oct 5, 2006

Chapter 20: Database System Architectures

Centralized and Client-Server Systems

Server System Architectures

Parallel Systems

Distributed Systems

Network Types




Centralized Systems

Run on a single computer system and do not interact with other

computer systems. General-purpose computer system: one to a few CPUs and a number

of device controllers that are connected through a common bus thatprovides access to shared memory.

Single-user system (e.g., personal computer or workstation): desk-top

unit, single user, usually has only one CPU and one or two harddisks; the OS may support only one user.

Multi-user system: more disks, more memory, multiple CPUs, and amulti-user OS. Serve a large number of users who are connected tothe system vie terminals. Often called server systems.




A Centralized Computer System




Client-Server Systems

Server systems satisfy requests generated at m client systems, whose general

structure is shown below:




Client-Server Systems (Cont.)

Database functionality can be divided into:

Back-end: manages access structures, query evaluation andoptimization, concurrency control and recovery.

Front-end: consists of tools such as forms , report-writers , andgraphical user interface facilities.

The interface between the front-end and the back-end is through SQL or

through an application program interface.


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Client-Server Systems (Cont.)

Advantages of replacing mainframes with networks of workstations or

personal computers connected to back-end server machines: better functionality for the cost

flexibility in locating resources and expanding facilities

better user interfaces

easier maintenance



Server System Architecture

Server systems can be broadly categorized into two kinds:

transaction servers which are widely used in relational databasesystems, and

data servers, used in object-oriented database systems



Transaction Servers

Also called query server systems or SQL server systems

Clients send requests to the server

Transactions are executed at the server

Results are shipped back to the client.

Requests are specified in SQL, and communicated to the serverthrough a remote procedure call (RPC) mechanism.

Transactional RPC allows many RPC calls to form a transaction.

Open Database Connectivity (ODBC) is a C language applicationprogram interface standard from Microsoft for connecting to a server,sending SQL requests, and receiving results.

JDBC standard is similar to ODBC, for Java



Transaction Server Process Structure

A typical transaction server consists of multiple processes accessing

data in shared memory. Server processes

These receive user queries (transactions), execute them and sendresults back

Processes may be multithreaded, allowing a single process to

execute several user queries concurrently Typically multiple multithreaded server processes

Lock manager process

More on this later

Database writer process

Output modified buffer blocks to disks continually



Transaction Server Processes (Cont.)

Log writer process

Server processes simply add log records to log record buffer

Log writer process outputs log records to stable storage.

Checkpoint process

Performs periodic checkpoints

Process monitor process Monitors other processes, and takes recovery actions if any of the other

processes fail

E.g. aborting any transactions being executed by a server processand restarting it



Transaction System Processes (Cont.)





Data Servers

Used in high-speed LANs, in cases where

The clients are comparable in processing power to the server

The tasks to be executed are compute intensive.

Data are shipped to clients where processing is performed, and thenshipped results back to the server.

This architecture requires full back-end functionality at the clients.

Used in many object-oriented database systems

Issues:

Page-Shipping versus Item-Shipping

Locking

Data Caching

Lock Caching



Data Servers (Cont.)

Page-shipping versus item-shipping

Smaller unit of shipping more messages Worth prefetching related items along with requested item

Page shipping can be thought of as a form of prefetching

Locking

Overhead of requesting and getting locks from server is high due

to message delays Can grant locks on requested and prefetched items; with page

shipping, transaction is granted lock on whole page.

Locks on a prefetched item can be P{called back} by the server,and returned by client transaction if the prefetched item has notbeen used.

Locks on the page can be deescalated to locks on items in thepage when there are lock conflicts. Locks on unused items canthen be returned to server.



Data Servers (Cont.)

Data Caching

Data can be cached at client even in between transactions

But check that data is up-to-date before it is used (cache coherency)

Check can be done when requesting lock on data item

Lock Caching

Locks can be retained by client system even in between transactions Transactions can acquire cached locks locally, without contacting

server

Server calls back locks from clients when it receives conflicting lockrequest. Client returns lock once no local transaction is using it.

Similar to deescalation, but across transactions.



Parallel Systems

Parallel database systems consist of multiple processors and multiple

disks connected by a fast interconnection network. A coarse-grain parallel machine consists of a small number of

powerful processors

A massively parallel or fine grain parallel machine utilizesthousands of smaller processors.

Two main performance measures: throughput --- the number of tasks that can be completed in a

given time interval

response time --- the amount of time it takes to complete a singletask from the time it is submitted



Speed-Up and Scale-Up

Speedup: a fixed-sized problem executing on a small system is given

to a system which is N -times larger. Measured by:

speedup = small system elapsed time

large system elapsed time

Speedup is linear if equation equals N.

Scaleup: increase the size of both the problem and the system

N -times larger system used to perform N -times larger job

Measured by:

scaleup = small system small problem elapsed time

big system big problem elapsed time

Scale up is linear if equation equals 1.



Speedup

Speedup



Scaleup

Scaleup



Batch and Transaction Scaleup

Batch scaleup:

A single large job; typical of most decision support queries andscientific simulation.

Use an N -times larger computer on N -times larger problem.

Transaction scaleup:

Numerous small queries submitted by independent users to a

shared database; typical transaction processing and timesharingsystems.

N -times as many users submitting requests (hence, N -times asmany requests) to an N -times larger database, on an N -timeslarger computer.

Well-suited to parallel execution.



Factors Limiting Speedup and Scaleup

Speedup and scaleup are often sublinear due to:

Startup costs: Cost of starting up multiple processes may dominatecomputation time, if the degree of parallelism is high.

Interference: Processes accessing shared resources (e.g.,systembus, disks, or locks) compete with each other, thus spending timewaiting on other processes, rather than performing useful work.

Skew: Increasing the degree of parallelism increases the variance inservice times of parallely executing tasks. Overall execution timedetermined by slowest of parallely executing tasks.




Interconnection Network Architectures

Bus. System components send data on and receive data from asingle communication bus;

Does not scale well with increasing parallelism.

Mesh. Components are arranged as nodes in a grid, and eachcomponent is connected to all adjacent components

Communication links grow with growing number of components,and so scales better.

But may require 2n hops to send message to a node (or n withwraparound connections at edge of grid).

Hypercube. Components are numbered in binary; components areconnected to one another if their binary representations differ inexactly one bit.

n components are connected to log(n) other components and canreach each other via at most log(n) links; reduces communicationdelays.




Interconnection Architectures




Parallel Database Architectures

Shared memory -- processors share a common memory

Shared disk -- processors share a common disk Shared nothing -- processors share neither a common memory nor

common disk

Hierarchical -- hybrid of the above architectures




Parallel Database Architectures




Shared Memory

Processors and disks have access to a common memory, typically via

a bus or through an interconnection network. Extremely efficient communication between processors — data in

shared memory can be accessed by any processor without having tomove it using software.

Downside – architecture is not scalable beyond 32 or 64 processorssince the bus or the interconnection network becomes a bottleneck

Widely used for lower degrees of parallelism (4 to 8).




Shared Disk

All processors can directly access all disks via an interconnection

network, but the processors have private memories. The memory bus is not a bottleneck

Architecture provides a degree of fault-tolerance — if a processorfails, the other processors can take over its tasks since the databaseis resident on disks that are accessible from all processors.

Examples: IBM Sysplex and DEC clusters (now part of Compaq)running Rdb (now Oracle Rdb) were early commercial users

Downside: bottleneck now occurs at interconnection to the disksubsystem.

Shared-disk systems can scale to a somewhat larger number of

processors, but communication between processors is slower.




Shared Nothing

Node consists of a processor, memory, and one or more disks.Processors at one node communicate with another processor atanother node using an interconnection network. A node functions asthe server for the data on the disk or disks the node owns.

Examples: Teradata, Tandem, Oracle-n CUBE

Data accessed from local disks (and local memory accesses) do notpass through interconnection network, thereby minimizing theinterference of resource sharing.

Shared-nothing multiprocessors can be scaled up to thousands ofprocessors without interference.

Main drawback: cost of communication and non-local disk access;sending data involves software interaction at both ends.




Hierarchical

Combines characteristics of shared-memory, shared-disk, and shared-nothing architectures.

Top level is a shared-nothing architecture – nodes connected by aninterconnection network, and do not share disks or memory with eachother.

Each node of the system could be a shared-memory system with afew processors.

Alternatively, each node could be a shared-disk system, and each ofthe systems sharing a set of disks could be a shared-memory system.

Reduce the complexity of programming such systems by distributedvirtual-memory architectures

Also called non-uniform memory architecture (NUMA)




Distributed Systems

Data spread over multiple machines (also referred to as sites ornodes).

Network interconnects the machines

Data shared by users on multiple machines




Distributed Databases

Homogeneous distributed databases

Same software/schema on all sites, data may be partitionedamong sites

Goal: provide a view of a single database, hiding details ofdistribution

Heterogeneous distributed databases

Different software/schema on different sites

Goal: integrate existing databases to provide useful functionality

Differentiate between local and global transactions

A local transaction accesses data in the single site at which thetransaction was initiated.

A global transaction either accesses data in a site different fromthe one at which the transaction was initiated or accesses data inseveral different sites.




Trade-offs in Distributed Systems

Sharing data – users at one site able to access the data residing atsome other sites.

Autonomy – each site is able to retain a degree of control over datastored locally.

Higher system availability through redundancy — data can bereplicated at remote sites, and system can function even if a site fails.

Disadvantage: added complexity required to ensure propercoordination among sites.

Software development cost.

Greater potential for bugs.

Increased processing overhead.

Implementation Issues for Distributed




Implementation Issues for Distributed

Databases

Atomicity needed even for transactions that update data at multiple sites

The two-phase commit protocol (2PC) is used to ensure atomicity

Basic idea: each site executes transaction until just before commit,and the leaves final decision to a coordinator

Each site must follow decision of coordinator, even if there is a failurewhile waiting for coordinators decision

2PC is not always appropriate: other transaction models based onpersistent messaging, and workflows, are also used

Distributed concurrency control (and deadlock detection) required

Data items may be replicated to improve data availability

Details of above in Chapter 22




Network Types

Local-area networks (LANs) – composed of processors that aredistributed over small geographical areas, such as a single building ora few adjacent buildings.

Wide-area networks (WANs) – composed of processors distributedover a large geographical area.




Networks Types (Cont.)

WANs with continuous connection (e.g. the Internet) are needed forimplementing distributed database systems

Groupware applications such as Lotus notes can work on WANs withdiscontinuous connection:

Data is replicated.

Updates are propagated to replicas periodically.

Copies of data may be updated independently.

Non-serializable executions can thus result. Resolution isapplication dependent.



Database System Concepts, 5th Ed.

©Silberschatz, Korth and SudarshanSee www.db-book.com for conditions on re-use

End of Chapter





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